增强型海鸥优化算法及其在动态优化问题中的应用

Enhanced seagull optimization algorithm and its application in dynamic optimization problems

近年来群智能优化算法因其原理简单且具有良好的全局寻优性能,在控制向量参数化方法的框架下,被广泛应用于化工动态优化问题的求解。为进一步提高化工动态优化问题的求解精度,提出一种增强型海鸥优化算法。该算法采用约束初始化思想来生成初始种群,引入动态收敛因子优化海鸥的攻击模式,以及将Levy飞行策略与动态收敛因子结合并应用到海鸥位置的更新过程,以增强算法跳出局部最优的能力。最后,将提出的改进算法应用于管式反应器的平行反应和批式反应器的优化求解,并与其他智能优化算法以及相关文献的优化结果进行对比分析。实验结果表明,增强型海鸥优化算法可以取得有竞争力的结果,算法具有较强的寻优性能。

In recent years,in allusion to the simple principle and good global optimization performance of the swarm intelligence optimization algorithm,it has been widely used to solve chemical dynamic optimization problems under the framework of the control vector parameterization method.An enhanced seagull optimization algorithm(ESOA)is proposed to further improve the solution accuracy of chemical dynamic optimization problems.This algorithm can be used to generate the initial population by means of the idea of constraint initialization,introduce a dynamic convergence factor to optimize the attack mode of seagulls,and combine the Levy flight strategy with the dynamic convergence factor and apply it to the update process of seagull positions,so as to enhance the algorithm′s ability to jump out of local optima.The proposed improved algorithm is applied to the optimization of the parallel reaction of tubular reactors and the batch reactors,and compare and analyze the optimization results with other intelligent optimization algorithms and relevant literature.The experimental results show that the enhanced seagull optimization algorithm can achieve competitive results and has strong optimization performance.